Thank you Julia community

I’d like to say a big thank you to the creators and developers of Julia. The year 2016, if disappointing in some ways (!), has though been a good year for us Julia users!

What I like about Julia is that it’s easy to use, powerful, fast, and free. I haven’t found many programming languages that score highly in all four areas. The good thing about it being easy to use is that you don’t have to be a fully-fledged computer scientist to get some results immediately.

And that’s also why it’s great that Julia is also powerful and fast - you get reasonable performance out of the box, and often you don’t need to spend hours fine-tuning your code to make it run fast (and there’s plenty of scope for more advanced users to turbocharge their code). Because it’s designed for technical computing, it can easily handle my more day-to-day scripting and computing tasks. I also appreciate that it’s free…!

So thank you Julia creators and developers, and everyone who puts their effort into this great project. 2017 looks to be another good year for Julia.

I coded a small seasonal animation to while away a few winter hours over Christmas - you can find it here (with apologies for the hemispherism of suggesting that it’s snowing outside, which even here in my corner of the Northern Hemisphere it usually isn’t).

It was much more interesting to write than it will be to watch, but at least it records the top 100 or so Julia contributors in 2016, and the music is cool.


A few times I have seen someone mention that there is a lot of thankless work in Julia, so I’d like to post this reminder that I am extremely thankful for all of the time that others have put into Julia and its ecosystem.

It’s truly a gift to the world! Thank you so much…


Time to dig up this thread again and say it over and over.

Here in 2020 where we’re all dealing with COVID and whatever, I am in dependency hell on a project I’m collaborating on in R. There are literally maybe 100 random packages that need to be installed from two separate installation mechanisms (CRAN and Bioconductor). The whole thing is a mess of “run the code, see if it works, see what breaks, find the package, install the package, repeat”. I’ve spent around a full day of work on this over the last few weeks. I’m pretty sure that once I’m done, I’ll never be able to do anything in any R project I’ve worked on in the past anymore because incompatible versions of packages have been pulled in… and even if that doesn’t happen, it’s only by accident, not by design.

Julia and its Project.toml files etc makes getting a working installation of packages that doesn’t bork your main install doable… not just doable but really frikin easy and awesome. Plus you can actually write Julia code and it runs… not like R where you really need to write C code and then call it from R.

Thanks again everyone!

(now back to install hell)